CLAP-HMM: a biologically constrained deep learning framework for resistance gene prediction in long DNA sequences.

Liuyan Wang, Yingfan Xu, Xuemei Guan, Shanchun Yan

Abstract read

In one paragraph

Article in BMC biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Liuyan Wang *College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang, 150040, China.

Yingfan Xu *College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang, 150040, China.

Xuemei GuanCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang, 150040, China. gxm_maomao1980@nefu.edu.cn.

Shanchun YanKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin, Heilongjiang, 150040, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Resistance (R) genes play a critical role in defending against pathogenic infections, and identifying novel R genes is a key component of resistance breeding. However, current computational methods face significant limitations when predicting R genes from ultra-long, highly repetitive genomic sequences. In this study, we propose a deep learning framework, CLAP-HMM, which employs a CNN-LSTM-Attention fusion strategy to extract gene expression patterns from long DNA sequences and accurately predict R genes. Our approach introduces protein function hints as biological priors to guide the HMM module in structural state modeling, enabling precise identification of R gene boundaries. Comparative experiments demonstrate that CLAP-HMM outperforms existing methods in discovering more potential novel R genes, offering promising prospects for uncovering biologically meaningful functional genes and advancing our understanding of disease resistance mechanisms.

Indexed as

Computational BiologyDeep LearningDisease ResistanceSequence Analysis, DNAConvolutional Neural NetworksPrediction AlgorithmsBiological prior constraintCNN-LSTM-AttentionDeep learningLong sequence modelingResistance gene prediction

Identifiers

PMID41382144

DOI10.1186/s12896-025-01086-x

PMCPMC12809951

What Socratic holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.